Physicians are required to make many medical decisions ranging from, for example, whether and when a patient is likely to experience a medical condition to how a patient should be treated once the patient has been diagnosed with the condition. Determining an appropriate course of treatment for a patient may increase the patient's chances for, for example, survival, recovery, and/or improved quality of life. Predicting the occurrence of an event also allows individuals to plan for the event. For example, predicting whether a patient is likely to experience occurrence (e.g., presence, recurrence, or progression) of a disease may allow a physician to recommend an appropriate course of treatment for that patient.
When a patient is diagnosed with a medical condition, deciding on the most appropriate therapy is often confusing for the patient and the physician, especially when no single option has been identified as superior for overall survival and quality of life. Traditionally, physicians rely heavily on their expertise and training to treat, diagnose and predict the occurrence of medical conditions. For example, pathologists use the Gleason scoring system to evaluate the level of advancement and aggression of prostate cancer, in which cancer is graded based on the appearance of prostate tissue under a microscope as perceived by a physician. Higher Gleason scores are given to samples of prostate tissue that are more undifferentiated. Although Gleason grading is widely considered by pathologists to be reliable, it is a subjective scoring system. Particularly, different pathologists viewing the same tissue samples may make conflicting interpretations.
Current preoperative predictive tools have limited utility for the majority of contemporary patients diagnosed with organ-confined and/or intermediate risk disease. For example, prostate cancer remains the most commonly diagnosed non-skin cancer in American men and causes approximately 29,000 deaths each year [1]. Treatment options include radical prostatectomy, radiotherapy, and watchful waiting; there is, however, no consensus on the best therapy for maximizing disease control and survival without over-treating, especially for men with intermediate-risk prostate cancer (prostate-specific antigen 10-20 ng/mL, clinical stage T2b-c, and Gleason score 7). The only completed, randomized clinical study has demonstrated lower rates of overall death in men with T1 or T2 disease treated with radical prostatectomy; however, the results must be weighed against quality-of-life issues and co-morbidities [2, 3]. It is fairly well accepted that aggressive prostate-specific antigen (PSA) screening efforts have hindered the general utility of more traditional prognostic models due to several factors including an increased (over-diagnosis) of indolent tumors, lead time (clinical presentation), grade inflation and a longer life expectancy [4-7]. As a result, the reported likelihood of dying from prostate cancer 15 years after diagnosis by means of prostate-specific antigen (PSA) screening is lower than the predicted likelihood of dying from a cancer diagnosed clinically a decade or more ago further confounding the treatment decision process [8].
Several groups have developed methods to predict prostate cancer outcomes based on information accumulated at the time of diagnosis. The recently updated Partin tables [9] predict risk of having a particular pathologic stage (extracapsular extension, seminal vesicle invasion, and lymph node invasion), while the 10-year preoperative nomogram [10] provides a probability of being free of biochemical recurrence within 10 years after radical prostatectomy. These approaches have been challenged due to their lack of diverse biomarkers (other than PSA), and the inability to accurately stratify patients with clinical features of intermediate risk. Since these tools rely on subjective clinical parameters, in particular the Gleason grade which is prone to disagreement and potential error, having more objective measures would be advantageous for treatment planning. Furthermore, biochemical or PSA recurrence alone generally is not a reliable predictor of clinically significant disease [11]. Thus, it is believed by the present inventors that additional variables or endpoints are required for optimal patient counseling.
Another issue is unnecessary treatment for disease. For example, a man with a truly indolent tumor, typically understood to mean a tumor that is not likely to progress, who is misclassified as having a nonindolent tumor can be subjected to treatments that potentially carry severe side effects. Thus, the need for accurate predictive models for indolent disease status is crucial. Traditionally, research regarding indolent tumors has addressed only standard clinical features. Kattan et al. reported that in a sample of 409 patients, about 20% had indolent cancer [56]. The Kattan predictive nomograms incorporated biopsy Gleason grade, clinical stage and pre-treatment PSA along with the amount of tumor in the biopsy specimen. The reported predictive power of the nomograms ranged from an AUC of 0.64 for the ROC curve to an AUC of 0.79. Gibod et al., while identifying 29% of patients as having “insignificant cancer,” reported that no specific feature in their study could identify the indolent patients prior to surgery [57]. Gofrit et al. studied cases of “Gleason score upgrades,” where the patient has a biopsy Gleason score of 6 but is actually harboring a more aggressive tumor. It was suggested that both preoperation PSA levels and the greatest percent cancer in a core (GPC) can predict the phenomenon of Gleason score upgrade by employing decision tree methodology [58]. The reported overall accuracy was about 62%.
Ochiai et al. also purported to predict so-called “insignificant cancer” by using the number of positive biopsy cores, tumor length in a core, Gleason score and prostate volume in a multiple logistic regression model. The reported sensitivity and specificity was about 84% and 62%, respectively [59]. Similar results were reported in another study by Ochiai and associates [60]. Romelling et al. updated the Kattan nomogram and reported in a study of 432 cancer patients that 27% were classified as having indolent tumors [61]. The percentage of patients reported to have indolent tumors was typically in the 20-30% range in the studies that have been cited. It is believed by the present inventors that a more accurate, stable and comprehensive approach to predicting whether or not a disease (e.g., cancer) is indolent is needed.
In view of the foregoing, it would be desirable to provide systems and methods for treating, diagnosing and predicting the occurrence of medical conditions, responses, and other medical phenomena with improved predictive power. For example, it would be desirable to provide systems and methods for predicting the likelihood that a disease (e.g., cancer) is indolent and/or a risk of disease progression at, for example, the time of diagnosis prior to treatment for the disease.